Feature Selection Based on Mutual Correlation

  • Michal Haindl
  • Petr Somol
  • Dimitrios Ververidis
  • Constantine Kotropoulos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)


Feature selection is a critical procedure in many pattern recognition applications. There are two distinct mechanisms for feature selection namely the wrapper methods and the filter methods. The filter methods are generally considered inferior to wrapper methods, however wrapper methods are computationally more demanding than filter methods. A novel filter feature selection method based on mutual correlation is proposed. We assess the classification performance of the proposed filter method by using the selected features to the Bayes classifier. Alternative filter feature selection methods that optimize either the Bhattacharrrya distance or the divergence are also tested. Furthermore, wrapper feature selection techniques employing several search strategies such as the sequential forward search, the oscillating search, and the sequential floating forward search are also included in the comparative study. A trade off between the classification accuracy and the feature set dimensionality is demonstrated on both two benchmark datasets from UCI repository and two emotional speech data collections.


Feature Selection Feature Selection Method Feature Selection Algorithm Mutual Correlation Forward Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Michal Haindl
    • 1
  • Petr Somol
    • 1
  • Dimitrios Ververidis
    • 2
  • Constantine Kotropoulos
    • 2
  1. 1.Academy of Sciences CRInstitute of Information Theory and AutomationPragueCzech Republic
  2. 2.Dept. of InformaticsAristotle Univ. of ThessalonikiThessalonikiGreece

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